{"id":"W2148558022","doi":"10.1109/mwscas.2007.4488742","title":"Design-specific supply and threshold voltage optimization in nanometer era","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cadence; Threshold voltage; Voltage; Computer science; Power–delay product; Leakage (economics); Power optimization; Dynamic voltage scaling; Power (physics); Very-large-scale integration; Nonlinear system; Electronic engineering; Optimal design; Energy (signal processing); Integrated circuit design; Efficient energy use; Mathematical optimization; Control theory (sociology); Electrical engineering; Transistor; Engineering; Mathematics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004381813,0.0002226744,0.0002031074,0.0003370603,0.00005091705,0.0001685933,0.0001796395,0.0001380267,0.0001325803],"category_scores_gemma":[0.00001322098,0.0002251752,0.00001793702,0.0004156731,0.00006283734,0.0007206526,0.00003676614,0.0002544458,0.00002892664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008676264,"about_ca_system_score_gemma":0.00001769468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000220303,"about_ca_topic_score_gemma":0.000001709755,"domain_scores_codex":[0.9988115,0.000001980546,0.0002985175,0.0002795657,0.0001714309,0.0004370269],"domain_scores_gemma":[0.9996364,0.00003158894,0.00003598547,0.00009092352,0.0001023148,0.000102842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003570321,0.0001924114,0.1507573,0.0009210954,0.00009519367,0.00008667564,0.02693377,0.04843888,0.6708691,0.01289375,0.008236106,0.0802187],"study_design_scores_gemma":[0.002269959,0.0002678479,0.03755921,0.0004416314,0.00002437978,0.00005056759,0.001037585,0.7823164,0.169177,0.001062152,0.004266897,0.001526349],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5329189,0.0004199637,0.4609713,0.00003540453,0.00019773,0.0003874936,0.000001525745,0.000289955,0.004777652],"genre_scores_gemma":[0.9794396,0.0004971122,0.01980888,0.00003365504,0.0000591994,0.00002875031,0.000004034324,0.00004198744,0.0000867693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7338776,"threshold_uncertainty_score":0.9182378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01954620501254578,"score_gpt":0.204957039047041,"score_spread":0.1854108340344952,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}